[1]} searched for hea having high hardness using 135 training data samples (18 experimentally from their lab) and demonstrated that learning from composition and descriptors exploiting hea domain knowledge outperformed ml models that use only compositional descriptors. They compared performance across several different models (lr, pr, svm, dt, ann, and knn), for which svm with a rbf kernel had the best performance on test data (fig:wen2019-model-compare-b). The svm surrogate model was used in a doe-based ad scheme and fs was performed via a hybrid ca/wrapper. Using arc melting, they synthesized 42 alloys, 35 of them having higher hardness than the hardest candidates of the training set, 17 of them having \(\sim \) 10 higher hardness, and the highest with \(\sim \) 14 higher hardness (883(47) HV relative to 775 HV). They suggested extending this framework to bulk metallic glasses and superalloys.
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